Influence of short‐term variations of meteorological parameters on sound propagation outdoors

2006 ◽  
Vol 120 (5) ◽  
pp. 3335-3335
Author(s):  
Philippe Blanc‐Benon ◽  
Benjamin Cotte ◽  
Benoit Gauvreau ◽  
Michel Berengier
Keyword(s):  
Meteorological Parameters ◽  
Sound Propagation ◽  
Short Term

scholarly journals Exploring short term spatio-temporal pattern of PM2.5 and PM10 and their relationship with meteorological parameters during COVID-19 in Delhi

Urban Climate ◽  
2021 ◽  
pp. 100944
Author(s):  
Manob Das ◽  
Arijit Das ◽  
Raju Sarkar ◽  
Papiya Mandal ◽  
Sunil Saha ◽  
...  
Keyword(s):  
Temporal Pattern ◽  
Meteorological Parameters ◽  
Short Term ◽  
Spatio Temporal ◽  
Pm2.5 And Pm10

scholarly journals Accounting for local meteorological effects in the ozone time-series of Lovozero (Kola Peninsula)

2003 ◽  
Vol 3 (4) ◽  
pp. 941-949 ◽  
Author(s):  
O. A. Tarasova ◽  
A. Yu. Karpetchko
Keyword(s):  
Regression Model ◽  
Kola Peninsula ◽  
Meteorological Parameters ◽  
Surface Ozone ◽  
Meteorological Condition ◽  
Cold Season ◽  
Meteorological Conditions ◽  
Short Term ◽  
The Relationship ◽  
Meteorological Effects

Abstract. The relationship between local meteorological conditions and the surface ozone variability was studied by means of statistical modeling, using ozone and meteorological parameters measured at Lovozero (250 m a.s.l., 68.5°N, 35.0°E, Kola Peninsula) for the period of 1999-2000. The regression model of daily mean ozone concentrations on such meteorological parameters as temperature, relative humidity and wind speed explains up to 70% of day-to-day ozone variability in terms of meteorological condition changes, if the seasonal cycle is also considered. A regression model was created for separated time scales of the variables. Short-term, synoptical and seasonal components are separated by means of Kolmogorov-Zurbenko filtering. The synoptical scale variations were chosen as the most informative from the point of their mutual relation with meteorological parameters. Almost 40% of surface ozone variations in time periods of 11-60 days can be explained by the regression model on separated scales that is 30% more efficient than ozone residuals usage. Quantitative and qualitative estimations of the relations between surface ozone and meteorological predictors let us preliminarily conclude that at the Lovozero site surface ozone variability is governed mainly by dynamical processes of various time scale rather than photochemistry, especially during the cold season.

scholarly journals Monitoring of 7Be atmospheric activity concentration using short term measurements

2011 ◽  
Vol 26 (2) ◽  
pp. 101-109 ◽  
Author(s):  
Sotiria Papandreou ◽  
Marilia Savva ◽  
Konstantinos Karfopoulos ◽  
Dimitrios Karangelos ◽  
Marios Anagnostakis ◽  
...  
Keyword(s):  
Atmospheric Aerosol ◽  
Meteorological Parameters ◽  
Activity Concentration ◽  
Sampling Interval ◽  
Nuclear Engineering ◽  
Type B ◽  
Physical Processes ◽  
Short Term ◽  
Engineering Department

The activity concentration of 7Be in atmospheric aerosol can exhibit seasonal variations due to various physical processes taking place in the troposphere and stratosphere, as well as due to solar activity. An investigation of these variations has been carried out at the Nuclear Engineering Department of the National Technical University of Athens over a two year period (3/2008-4/2010). In the framework of this study, sampling and analysis methods were appropriately selected to allow for the observation of short-term 7Be air activity concentration variations, using a 4-hour sampling interval, while taking in consideration type A and type B uncertainties introduced in the measurements. In order to study the role of precipitation in surface air 7Be activity concentration variations, a procedure for collecting and analyzing rainwater was developed. The techniques used in the present study allowed for the observation of seasonal and diurnal 7Be concentration variations, as well as correlations between 7Be activity concentration and the meteorological parameters of air temperature and relative humidity.

Measurement of atmospheric sound propagation and meteorological parameters to support development of an acoustic propagation model

2016 ◽  
Vol 140 (4) ◽  
pp. 3260-3260
Author(s):  
Melissa A. Hall ◽  
Teresa Ryan ◽  
Seth Hubbard ◽  
Joseph F. Vignola ◽  
John A. Judge ◽  
...  
Keyword(s):  
Meteorological Parameters ◽  
Sound Propagation ◽  
Acoustic Propagation ◽  
Propagation Model

A short-term and long-term relationship between occurrence of acute canine babesiosis and meteorological parameters in Belgrade, Serbia

2019 ◽  
Vol 10 (6) ◽  
pp. 101273
Author(s):  
Filip Janjić ◽  
Darko Sarvan ◽  
Snežana Tomanović ◽  
Jelena Ćuk ◽  
Vanja Krstić ◽  
...  
Keyword(s):  
Meteorological Parameters ◽  
Canine Babesiosis ◽  
Short Term

scholarly journals Bootstrap Aggregating Approach to Short-Term Load Forecasting Using Meteorological Parameters for Demand Side Management in The North-Eastern Region of India

2021 ◽  
Author(s):  
Dipu Sarkar ◽  
Taliakum AO ◽  
Sravan Kumar Gunturi
Keyword(s):  
Machine Learning ◽  
Power Plants ◽  
Meteorological Parameters ◽  
Load Forecasting ◽  
Data Availability ◽  
Production Costs ◽  
Eastern Region ◽  
Short Term ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract Electricity is an essential commodity that must be generated in response to demand. Hydroelectric power plants, fossil fuels, nuclear energy, and wind energy are just a few examples of energy sources that significantly impact production costs. Accurate load forecasting for a specific region would allow for more efficient management, planning, and scheduling of low-cost generation units and ensuring on-time energy delivery for full monetary benefit. Machine learning methods are becoming more effective on power grids as data availability increases. Ensemble learning models are hybrid algorithms that combine various machine learning methods and intelligently incorporate them into a single predictive model to reduce uncertainty and bias. In this study, several ensemble methods were implemented and tested for short-term electric load forecasting. The suggested method is trained using the influential meteorological variables obtained through correlation analysis and the past load. We used real-time load data from Nagaland's load dispatch centre in India and meteorological parameters of the Nagaland region for data analysis. The synthetic minority over-sampling technique for regression (SMOTE-R) is also employed to avoid data imbalance issues. The experimental results show that the Bagging methods outperform other models with respect to mean squared error and mean absolute percentage error.

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